Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Afiya Ayman, Amutheezan Sivagnanam, Michael Wilbur, Philip Pugliese, Abhishek Dubey, Aron Laszka

ACM Transations of Internet Technology 2020

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Why This Matters

Public transit agencies operating mixed fleets of electric and internal combustion vehicles face significant challenges in optimizing operations while reducing environmental impact and operating costs. Accurate prediction of energy consumption is essential for effective vehicle scheduling and fleet management. This work is innovative because it provides an end-to-end framework that integrates real-world data collection, processing, and optimization to support practical decisions about vehicle assignments and fleet operation.

What We Did

This paper presents a comprehensive framework for data-driven prediction and optimization of energy consumption in transit fleets. The work integrates vehicle telemetry data, elevation information, weather conditions, and traffic data to build machine learning models for predicting energy consumption at the trip level. The framework includes algorithms for data cleaning, feature engineering, and optimization of vehicle-to-route assignments to minimize energy costs while meeting service constraints.

Key Results

The framework successfully demonstrates energy consumption prediction for mixed transit fleets using real data from the Chattanooga Area Regional Transportation Authority. Machine learning models including neural networks and decision trees achieve accurate energy predictions that outperform simpler baselines. Results show that the approach can support optimization of vehicle assignments to minimize energy costs while maintaining service levels, providing practical benefits for transit agencies operating electric vehicles.

Full Abstract

Cite This Paper

@article{aymantoit2020,
  author = {Ayman, Afiya and Sivagnanam, Amutheezan and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  journal = {ACM Transations of Internet Technology},
  title = {Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles},
  year = {2020},
  abstract = {Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal-combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes.  We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.},
  contribution = {colab},
  tag = {ai4cps,transit},
  keywords = {electric vehicles, energy consumption prediction, transit optimization, machine learning, vehicle scheduling, data-driven optimization}
}
Quick Info
Year 2020
Keywords
electric vehicles energy consumption prediction transit optimization machine learning vehicle scheduling data-driven optimization
Research Areas
energy transit ML for CPS
Search Tags

Data, Driven, Prediction, Optimization, Energy, Transit, Fleets, Electric, Vehicles, electric vehicles, energy consumption prediction, transit optimization, machine learning, vehicle scheduling, data-driven optimization, energy, transit, ML for CPS, 2020, Ayman, Sivagnanam, Wilbur, Pugliese, Dubey, Laszka